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Related Experiment Videos

T-S region-based fuzzy control with multiple performance constraints.

Sheng-Ming Wu1, Chein-Chung Sun, Wen-Jer Chang

  • 1Department of Electrical Engineering, National Central University, Chung-li 320, Taiwan, ROC.

ISA Transactions
|January 30, 2007
PubMed
Summary
This summary is machine-generated.

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This study introduces a T-S Region-based Fuzzy Model (TSRFM) and Controller (TSRFC) for robust control. The new method simplifies complex Takagi-Sugeno fuzzy systems, reducing controller rules while maintaining performance.

Area of Science:

  • Control Engineering
  • Fuzzy Systems
  • Robust Control Theory

Background:

  • Takagi-Sugeno (T-S) fuzzy control systems often involve numerous rules, complicating implementation and analysis.
  • Minimizing H2/Hinfinity norms is crucial for achieving desired performance and stability in control systems.
  • Existing methods may struggle with scalability for complex fuzzy models.

Purpose of the Study:

  • To develop a novel T-S Region-based Fuzzy Model (TSRFM) and Controller (TSRFC).
  • To simplify the implementation of T-S fuzzy control for systems with many plant rules.
  • To ensure robust stability and H2/Hinfinity performance with reduced complexity.

Main Methods:

  • Redesigning the T-S fuzzy model and controller using a fuzzy region concept.

Related Experiment Videos

  • Applying robust control techniques to stabilize plant rules within each fuzzy region.
  • Deriving stability conditions and H2/Hinfinity performance using Lyapunov criterion and Linear Matrix Inequalities (LMIs).

    • Main Results:

    • The proposed TSRFM and TSRFC significantly reduce the total number of LMIs and controller rules.
    • The TSRFC approach simplifies hardware implementation for fuzzy models with extensive plant rules.
    • Performance comparable to former designs is achieved despite the reduction in controller rules.

    Conclusions:

    • The TSRFM and TSRFC offer an effective strategy for simplifying complex Takagi-Sugeno fuzzy control problems.
    • This method enhances the practical implementability of robust fuzzy controllers without sacrificing performance.
    • The approach provides a scalable solution for robust H2/Hinfinity optimal control in complex systems.